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ChatGPT for Conversational AI and Chatbots

ChatGPT for Conversational AI and Chatbots

By : Adrian Thompson
5 (3)
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ChatGPT for Conversational AI and Chatbots

ChatGPT for Conversational AI and Chatbots

5 (3)
By: Adrian Thompson

Overview of this book

ChatGPT for Conversational AI and Chatbots is a definitive resource for exploring conversational AI, ChatGPT, and large language models. This book introduces the fundamentals of ChatGPT and conversational AI automation. You’ll explore the application of ChatGPT in conversation design, the use of ChatGPT as a tool to create conversational experiences, and a range of other practical applications. As you progress, you’ll delve into LangChain, a dynamic framework for LLMs, covering topics such as prompt engineering, chatbot memory, using vector stores, and validating responses. Additionally, you’ll learn about creating and using LLM-enabling tools, monitoring and fine tuning, LangChain UI tools such as LangFlow, and the LangChain ecosystem. You’ll also cover popular use cases, such as using ChatGPT in conjunction with your own data. Later, the book focuses on creating a ChatGPT-powered chatbot that can comprehend and respond to queries directly from your unique data sources. The book then guides you through building chatbot UIs with ChatGPT API and some of the tools and best practices available. By the end of this book, you’ll be able to confidently leverage ChatGPT technologies to build conversational AI solutions.
Table of Contents (15 chapters)
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1
Part 1: Foundations of Conversational AI
4
Part 2: Using ChatGPT, Prompt Engineering, and Exploring LangChain
9
Part 3: Building and Enhancing ChatGPT-Powered Applications

Why do we need RAG?

LLMs are restricted by their knowledge of the world through their training data, so ChatGPT doesn’t know about recent events or your own data, which severely restricts its ability to provide relevant answers. Things can also get worse with LLM performance because of hallucinations, where the LLM doesn’t have any knowledge to support a question, so it makes things up.

So, when we talk about an LLM’s knowledge, there are two types:

  • Knowledge from information that the LLM used during training.
  • Knowledge from information that was passed to the LLM via a prompt in the context of the conversation. We can call this context-specific knowledge.

So, the standout use case for an LLM application, and one that I’m asked about the most, is how we can allow an LLM to interpret and discuss data outside of their training dataset. This includes accessing real-time information or other external data sources, such as proprietary information...

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